Internet Of Things Deployments Research Articles

Enhancing service availability and resource deployment in IoT using a shared service replication method

In many real-world contexts, the Internet of Things (IoT) is valued for its capacity to facilitate the smooth operation of interoperable applications and services. It is critical to ensure the accessibility and replication of IoT resources to improve the agility of these applications. As a solution, the Network Function Virtualization (NFV) paradigm is embedded into the IoT design to leverage information from various endpoint applications better and maximize resource utilization. In this study, the Shared Replication Augmenting Method (SRAM) is proposed to increase resource usage in underutilized NFVs and maintain service availability simultaneously. The regressive decision-making learning used by SRAM enables the detection of NFV needs for data and application portability across various real-time use cases. This regression method can uncover data needs and their causes, allowing for prompt answers and more efficient use of available resources. The suggested SRAM technique dynamically modifies the procedure while considering computation-less function allocations, making it suitable for various interoperable applications. It distributes root-to-service virtualization and availability based on historical use and data replication. Therefore, SRAM improves resource usage by 7.09 % with no increase in latency or delays. It also increases service availability by 10.4 %, reduces latency by 11.89 %, eliminates backlogs by 11.1 %, and reduces data repetition by 8.97 %. This study enhances resource consumption and productivity in IoT settings by offering SRAM as a viable solution. The study's results prove its potential to reduce the occurrence of replication, delay, and queues while raising the availability of services.

Review of smart water management: IoT and AI in water and wastewater treatment

Integrating the Internet of Things (IoT) and Artificial Intelligence (AI) in smart water management revolutionizes sustainable water resource utilization. This comprehensive review explores these technologies' benefits, challenges, regulatory implications, and future trends. Smart water management enhances operational efficiency, predictive maintenance, and resource conservation while addressing data security and infrastructure investment challenges. Regulatory frameworks play a pivotal role in shaping the responsible deployment of AI and IoT, ensuring data privacy and ethical use. Future trends include advanced sensors, decentralized systems, quantum computing, and blockchain for enhanced water data security. The alignment with Sustainable Development Goals (SDGs) underscores the transformative potential of smart water management in achieving universal access to clean water, climate resilience, and inclusive, sustainable development. As we embrace these technologies, collaboration, public awareness, and ethical considerations will guide the evolution of intelligent and equitable water management systems.

Artificial Intelligence Driven Power Optimization in IOT-Enabled Wireless Sensor Networks

The widespread use of Wireless Sensor Networks (WSN) in Internet of Things (IoT) causes energy efficiency issues. This paper proposes an AI-based solution to this problem. The propose an AI-Driven Power Optimization framework for IoT-enabled WSN using Deep Q-Network (DQN) and Dynamic Voltage and Frequency Scaling (DVFS). These techniques can adapt to changing network conditions and reduce power consumption when used together. Sensor nodes provide environmental parameters, battery status, and network behavior data to the AI-driven framework DQN is implemented after data preprocessing to learn and make power management decisions using reinforcement learning. Neural network-driven agent operates in a state and action space. It optimizes energy use with rewards. Real-time hardware power adjustment is done using DVFS. DVFS precise control and AI-driven decision-making create a comprehensive power optimization strategy. AI adapts to new challenges and optimizes network lifespan by improving its power management policies. Experimental implementations of the proposed framework show significant energy savings, network lifespan extension, and QoS improvements. AI-Driven Power Optimization in IoT-enabled WSN is proven effective and flexible. This study shows the potential of AI, specifically DQN and DVFS, in IoT-enabled WSN. This AI-Driven Power Optimization framework addresses energy efficiency and improves IoT sensor networks. AI improves power optimization in IoT-enabled WSN making IoT deployments more sustainable and resilient.

REVIEW ON THE EVOLUTION AND IMPACT OF IOT-DRIVEN PREDICTIVE MAINTENANCE: ASSESSING ADVANCEMENTS, THEIR ROLE IN ENHANCING SYSTEM LONGEVITY, AND SUSTAINABLE OPERATIONS IN BOTH MECHANICAL AND ELECTRICAL REALMS

This study provides a comprehensive review of the evolution and impact of Internet of Things (IoT)-driven predictive maintenance, focusing on advancements in technology, their role in enhancing system longevity, and promoting sustainable operations in mechanical and electrical systems. The primary objective was to assess how IoT integration has transformed traditional maintenance approaches, leading to improved system durability and reliability. Utilizing a systematic literature review methodology, the study involved sourcing data from peer-reviewed journals, conference proceedings, and industry reports. A content analysis approach was employed to analyze the data, focusing on themes such as technological advancements, sustainability considerations, and industry-specific applications of IoT in predictive maintenance. Key findings reveal significant advancements in IoT applications, particularly the integration of advanced data analytics, artificial intelligence, and machine learning in predictive maintenance strategies. These advancements have led to more accurate and timely maintenance interventions, contributing to enhanced system longevity and operational efficiency. The study also highlights the emergence of green IoT practices and the challenges and opportunities in the future landscape of IoT in predictive maintenance. The study concludes that IoT-driven predictive maintenance is pivotal for sustainable industrial operations, with opportunities lying in addressing challenges through innovative solutions and robust regulatory frameworks. Recommendations for industry and policy include fostering sustainable IoT practices and prioritizing energy efficiency. Future research directions involve exploring the integration of IoT with emerging technologies and investigating the long-term environmental impacts of IoT deployments.
 Keywords: Predictive Maintenance, System Longevity, Sustainable Operations, Internet of Things.

Towards Efficient Sensor Deployment in Internet of Things for Target Coverage and Sensor Connectivity

Towards Efficient Sensor Deployment in Internet of Things for Target Coverage and Sensor Connectivity

A survey of security challenges, attacks in IoT

A network of linked devices that can gather, process, and exchange data is known as the Internet of Things (IoT). It delivers several benefits such as convenience, efficiency and automation. However, IoT also poses significant security concerns that necessity to be describe like the heterogeneity and diversity of IoT devices with different hardware, software and communication protocols. Now a days the large scale and complexity of IoT systems, which may involve multiple stakeholders, domains and layers. The limited resources and capabilities of IoT devices, which may affect their ability to perform security functions such as encryption, authentication and update. The vital and evolving features of IoT environments, which may introduce new threats and vulnerabilities over time. These challenges require novel and holistic approaches to ensure that privacy and security of IoT systems and its users. With the develop of security standards and best practices for IoT devices and systems it should include Implementation of security by design and security by default principles for IoT development and deployment by adopting risk-based and adaptive security mechanisms for IoT management, operation and enhanced security awareness and education among IoT users and stakeholders.

Investigating environmental sustainability applications using advanced monitoring systems

This study employs an Internet of Things (IoT)-based simulation to investigate environmental parameters critical to sustainability and public health. Over a 10-hour period, temporal variations in air quality parameters, including carbon dioxide (CO2), nitrogen dioxide (NO2), and particulate matter (PM2.5), were monitored. CO2 levels exhibited a decline from 400 ppm to 375 ppm, suggesting improvements in ventilation and reduced emissions. NO2 levels consistently decreased from 25 to 22 ppm, indicative of effective emissions control measures, while PM2.5 levels increased from 10 to 25 μg/m³, possibly influenced by transient factors. Water quality monitoring revealed fluctuations in dissolved oxygen (DO) levels (8.20–8.80 mg/L) and pH levels (7.00–7.35) over 10 hours, emphasizing the dynamic nature of aquatic ecosystems. Soil moisture levels stood at 29.48%, and energy consumption was recorded at 270 units, highlighting the importance of resource-efficient management. The significance of IoT-enabled monitoring in tracking and responding to environmental parameter changes, contributing to environmental sustainability and public health. Continuous data collection empowers stakeholders to make informed decisions for improved air and water quality, sustainable agriculture, and energy efficiency. Further research aims to enhance simulation realism and validate findings against real-world IoT deployments

Enhancing Security of 5G-Enabled IoT Systems through Advanced Authentication Mechanisms: A Multifaceted Approach

The Internet of Things (IoT) has revolutionized communication among devices, offering unprecedented efficiency and convenience. However, the widespread adoption of IoT has raised significant security concerns, emphasizing the need for robust security measures. This study focuses on the crucial aspect of authentication within the layered architecture of IoT systems. Authentication is foundational to the architecture, ensuring the availability, security, and integrity of IoT services and data. The research evaluates the current state of IoT authentication techniques, highlighting limitations in conventional solutions. To address these shortcomings, an advanced authentication framework is proposed, incorporating cutting-edge technologies such as blockchain, artificial intelligence, and biometrics. The framework employs biometric data for a dynamic and adaptive authentication process, enhancing security and accuracy in user and device identification. Blockchain technology is integrated to establish a decentralized and tamper-resistant identity management system, reducing the risk of unauthorized access and data manipulation. Artificial intelligence continuously adapts authentication processes based on behavioural patterns, bolstering the system's resilience against evolving cyber threats. The study also discusses the practical application of the proposed authentication system, considering resource limitations in IoT devices. It provides insights into the efficiency, scalability, and interoperability of the suggested solution within various IoT ecosystems. The research contributes to the ongoing discourse on IoT security by thoroughly examining enhanced authentication procedures. Organizations can fortify their IoT deployments against a growing array of cyber threats by prioritizing advanced authentication within a layered design, fostering a more secure and reliable IoT ecosystem. Additionally, the study presents a comprehensive overview of the state-of-the-art security in IoT, exploring various designs, enabling technologies, and protocols. It delves into security challenges at each architectural tier, providing an in-depth analysis of attack taxonomies and advanced defenses. The article serves as a valuable resource for researchers and academics in the IoT sector, offering a detailed survey of architectural security, identification of challenges, resolution strategies, and insights into the evolving landscape of IoT. The study provides a comprehensive survey of IoT architectural security, identifying challenges, proposing resolutions, and highlighting changes in the IoT domain. This research aims to enhance the accuracy by 80% of IoT security, fostering a more secure and reliable IoT ecosystem.

Challenges Associated with Wearable Internet-of-Things (IoTs) Monitoring Systems for E-Health

Wearable Internet-of-things (IoTs) monitoring systems provide several benefits that tend to secure diagnostic connection with health care providers thereby potentially influencing people’s orientation to accessing healthcare services. The goal of this overview is to explore the challenges associated with wearable Internet-of-Things (IoT) monitoring Systems for e-health. Wearable devices are integrated with IoT to enable health managers provide hospital setting to many localities, groups and individuals. Thus, wearable devices become imminent infrastructure for IoT deployment. To develop wearable-based IoT ecosystem, enabling techniques, architecture and other matters relating to IoT should be investigated and then their integration should be embraced. In this paper, we look at the challenges connected with wearable health monitors by system functionality, enabling technologies, security and privacy issues, and the prospect of integration of wearable technology and applications. Finally, we envisage that with the growth of e-health ideas, wearable devices will perform superior role in the arena of health care service and become well integrated into patients’ regular lives. Device interoperability, secured connection/communication to mitigate intermittent signal outages for point-to-point networks, providing advocacy in its usage among the generality of the populace and its deployment in requisite institutions – government and private, are some declared recommendations.

Enhancing IoT Network Reliability: Evaluating LoRa Module Susceptibility to Interference

The Internet of Things (IoT) is gaining popularity, leading to the widespread use of remote communication modules (LoRa), known for their energy efficiency and wide coverage range. However, as the number of LoRa modules used in IoT networks grows, the possibility of interference from third-party devices operating at the same frequency becomes a concern. This study aimed to examine the vulnerability of LoRa modules to electromagnetic interference (EMI) when transmitting text messages and images. Radiation emission conditions were measured in the test area for evaluating LoRa module performance, and susceptibility to interference was assessed under non-line-of-sight (NLOS) conditions. The study's outcomes reveal that interference with LoRa transmitters has no noticeable effect on the range within a distance of up to 50 meters. In contrast, the interference power required to disrupt the LoRa receiver decreases with increasing distance. Additionally, interference from frequencies outside the designated LoRa working frequency (915 MHz) has no discernible impact on module performance. Introducing a delivery delay check demonstrates consistent performance even in interference. These findings deepen our understanding of the susceptibility of LoRa modules to tampering, emphasizing the importance of implementing effective disruption management strategies in IoT deployments. By considering the potential impact of electromagnetic interference (EMI) on LoRa modules, developers can design more robust IoT networks, ensuring reliable communication and improved system performance. Overall, the research focuses on the interference characteristics of LoRa modules, providing insights for developing resilient and interference-resistant IoT solutions. It underscores the necessity of addressing interference issues to ensure the reliable operation of IoT devices across diverse environments.

Smart Agriculture: Technologies, Practices, and Future Directions

The Internet of Things (IoT) has ushered in a new era of innovative agriculture research. Because IoT is still in its early stages, it must be widely tested before it can be widely deployed in many agricultural applications. In this paper, I look at different prospective IoT applications, as well as the unique concerns and constraints connected with IoT deployment for better farming. The gadgets and wireless communication technologies linked with IoT in agricultural and farming applications are thoroughly researched to focus on the unique requirements. Sensor-enabled IoT systems that deliver intelligent and smart services for smart agriculture are being investigated.
 Several case studies are offered to investigate the existing IoT-based solutions implemented by various organizations, individuals, and groups depending on their deployment criteria. The difficulties in these solutions are addressed, as are the factors for improvement and the future roadmap of work with IoT. Smart farming is a trend that emphasizes the use of information and communications technology (ICT) in machinery, equipment, and sensors in network-based hi-tech farm supervision cycles. Innovative technologies such as the Internet of Things (IoT) and cloud computing are expected to spur growth and kickstart the usage of robotics and artificial intelligence in agriculture. Such radical departures are upsetting established agricultural practices while also posing several obstacles. This study explores the techniques and equipment utilized in wireless sensor applications in IoT agriculture, as well as the predicted problems encountered when integrating technology with traditional farming practices. Furthermore, this technical knowledge is useful to growers during crop times ranging from planting to harvest, and applications in packing and transportation are being researched.

Optimizing Data Processing and Security in Edge Computing For Iot Applications

Edge computing has emerged as a pivotal paradigm in the realm of Internet of Things (IoT) applications, addressing challenges of latency, bandwidth, and reliability by decentralizing computation and storage closer to the data source. This paper explores the critical aspects of optimizing data processing and enhancing security in edge computing environments to foster efficient and secure IoT deployments. In recent years, the proliferation of IoT devices has led to an exponential growth in data generation at the edge. Traditional cloud-centric approaches struggle to cope with the sheer volume and velocity of data produced by these devices, often resulting in latency issues and increased operational costs. Edge computing offers a promising solution by enabling data processing and analytics closer to where data is generated, thereby reducing latency and bandwidth consumption. The first part of this paper reviews the foundational concepts of edge computing, emphasizing its architectural principles and distinguishing characteristics in contrast to centralized cloud computing. Key optimization strategies for data processing at the edge are discussed, including edge analytics, data filtering, and compression techniques. These strategies aim to minimize data transmission to centralized cloud servers, thereby enhancing operational efficiency and real-time responsiveness in IoT applications.

Modelling Factors Influencing IoT Adoption: With a Focus on Agricultural Logistics Operations

Purpose- In recent years, there has been a notable surge in the utilization of emerging technologies, notably the Internet of Things (IoT), within the realm of business operations. However, empirical evidence has underscored a disconcerting trend whereby a substantial majority, surpassing 70%, of IoT adoption initiatives falter when confronted with the rigors of real-world implementation. Given the profound implications of IoT in augmenting product quality, this study endeavors to scrutinize the extant body of knowledge concerning IoT integration within the domain of agricultural logistics operations. Furthermore, it aims to discern the pivotal determinants that exert influence over the successful assimilation of IoT within business operations, with particular emphasis on logistics. Design/Methodology/Approach- The research utilizes a thorough systematic review methodology coupled with a meta-synthesis approach. In order to identify and clarify the key factors that influence IoT implementation in logistics operations, the study is grounded in the Resource-Based View theory. It employs rigorous grounded theory coding procedures, supported by the analytical capabilities of MAXQDA software. Findings- The culmination of the meta-synthesis endeavor culminates in the conceptual representation of IoT adoption within the agricultural logistics domain. This representation is underpinned by the identification of three overarching macro categories/constructs, namely: (1) IoT Technology Adoption, encompassing facets such as IoT implementation requisites, ancillary technologies essential for IoT integration, impediments encountered in IoT implementation, and the multifaceted factors that influence IoT adoption; (2) IoT-Driven Logistics Management, encompassing IoT-based warehousing practices, governance-related considerations, and the environmental parameters entailed in IoT-enabled logistics; and (3) the Prospective Gains Encompassing IoT Deployment, incorporating the financial, economic, operational, and sociocultural ramifications ensuing from IoT integration. The findings underscore the imperative of comprehensively addressing these factors for the successful assimilation of IoT within agricultural logistics processes. Originality- The originality of this research study lies in its pioneering effort to proffer a conceptual framework that furnishes a comprehensive panorama of the determinants that underpin IoT adoption, thereby ensuring its efficacious implementation within the ambit of agricultural logistics operations. Practical Implications- The developed framework, by bestowing upon stakeholders an incisive comprehension of the multifaceted factors that steer IoT adoption, holds the potential to streamline the IoT integration process. Moreover, it affords an avenue for harnessing the full spectrum of IoT-derived benefits within the intricate milieu of agricultural logistics operations.

Bi-Level Intrusion Detection in IoT Networks Using Ensemble Method and A-GRU-RNN Classifier

For minimizing security vulnerabilities and attacks, which affect Internet of Things (IoT) applications’ performance, it is essential to design an efficient authentication protocol owing to the rapid deployment of IoT. This paper proposes a bi-level Intrusion Detection (ID) in IoT using an ensemble and Arctan-based Gated Recurrent Unit-Recurrent Neural Network (A-GRU-RNN) classifier. This work mainly concentrates on both major and minor attacks. Allowing the trusted nodes to join the network is the other motive of this work. Here, the nodes are registered to the network and then initialized. Then, to verify the trust levels of the nodes, test packet transmission is performed. By employing the Deterministic Initialization Method-centric K-Means algorithm (DIM-K-Means) algorithm, the trusted nodes are formed into the cluster. Next, the respective CH is selected by Multiplex-Valued Encoding Sea Lion Optimization (MVE-SLO). Afterwards, by employing Multi-Point Relays-Optimized Link State Routing (MPR-OLSR), routing is taken place. The steps, namely preprocessing, attribute extraction, attribute reduction, and classification are taken to detect the confidentiality of the sensed data. The classification phase significantly determines whether the data is attacked or not. If the data is attacked, then it detects the attack type. Here, the publicly available dataset is used. As per the experimental outcome, the proposed method withstands high-security levels when analogized to the prevailing methodologies.

A lightweight IoT intrusion detection model based on improved BERT-of-Theseus

The proliferation of Internet of Things (IoT) technology has resulted in an increase in security vulnerabilities associated with the interconnectivity of IoT devices. As a result, there is a need for intrusion detection mechanisms that can effectively detect attacks on IoT security vulnerabilities. However, due to the resource constraints of IoT deployment devices, intrusion detection schemes must be customized to meet the specific demands of the IoT environment. In this study, we propose a knowledge-distillation-based IoT intrusion detection model named BT-TPF, which is capable of detecting network attacks encountered by IoT devices in an IoT environment with limited computing resources. The proposed BT-TPF model leverages a Siamese network for feature dimensionality reduction of complex high-dimensional network traffic data. Additionally, it employs a large-scale Vision Transformer as a teacher model to guide a small-scale Poolformer model during training, before deploying the trained Poolformer model as a classifier to detect network intrusion traffic. Through knowledge distillation, the final small model obtained in this paper only requires a minimum of 788 parameters, reducing the number of parameters by approximately 90% compared to the large model before knowledge distillation, while maintaining high detection accuracy. Experimental results show that the BT-TPF model achieves over 99% accuracy on both the CIC-IDS2017 and TON_IoT datasets. Furthermore, it exhibits significant advantages compared to traditional Deep Learning methods and recent state-of-the-art models, as evidenced by various evaluation metrics.

Impediment to implementation of Internet of Things (IOT) for oil and gas construction project Safety: Structural equation modeling approach

Several industries have adopted Internet of Things (IoT) technology extensively. Several impediments have impeded its widespread adoption in the oil and gas industry, making it challenging to supervise the safety of infrastructure projects involving these industries. The objective of this study was to catalogue and analyze the barriers to using IoT for ensuring safety on oil and gas development sites. The primary objective of the study was to develop a model of the barriers preventing the use of IoT for oil and gas safety management. Using a mixed-methods research strategy, the data collection and analysis were carried out. The survey information was analyzed using structural equation modelling (SEM) and exploratory factor analysis (EFA). Five formative constructs emerged from the EFA and were confirmed by structural equation modelling: technical, organizational, integration, economic, and efficiency. Using SEM's path analysis, we determined that all five structures have a substantial impact on how oil and gas companies manage the safety of construction sites when utilizing IoT. The findings of this study could inform efforts to improve the safety management of oil and gas construction sites through IoT. Using the framework presented in this study, organizations in the gas and energy industry can eliminate the barriers to IoT deployment in building safety management.

Evaluation of critical factors for the successful implementation of the Internet of Things based on PFN-Best Worst Method

Internet of Things (IoT) technology now has a new purpose and relevance as a result of the digitalization wave. In this setting, businesses start to plan how they will use IoT technology. But some critical factors can prevent the successful deployment of IoT, and businesses must get beyond these critical factors if they want to do so. The literature review, system literature review, and Delphi technique are used to identify 15 critical factors. These critical factors are then divided into four categories: organization, technology, process, and environment. The PFN-weighted power harmonic operator is proposed with the aim of more effectively obtaining assessment data from experts and lessening the inaccuracy of outcomes caused by information loss. The best and worst method (BWM) is used to determine the ideal weight of critical factors. Results indicate that the primary critical factors to the effective adoption of the Internet of Things are talent, resource limitations, integration complexity, technical operations, equipment power consumption, technical dependability, and data governance. This research will benefit corporate managers in recognizing the significance of the effective deployment of the Internet of Things, identifying major critical factors to this achievement, and making decisions to remove these factors. Thus, an organization may support the effective adoption of the animal Internet of Things.

An Assessment of Environmental RF Noise Due to IoT Deployment.

The advent of the Internet of Things (IoT) has contributed to an increase in the production volume of RF-featured equipment. According to statistics from the literature, the IoT industry will soon deploy billions of products. While the concept behind these applications seems exciting, this paper sought to assess the effects the radio emissions produced by IoT products would have on the ambient radio noise levels within the unlicensed frequency bands of 433 MHz, 868 MHz, and 2.4 GHz. The study extended to three environments: industrial, urban, and suburban. This study developed an IoT noise generator (ING) device to emulate RF noise signals in the desired IoT radio transmission band. The paper presents a simplified radio noise surveying system (RNSS) for data collection of ambient radio noise from five South African candidate sites. The statistical and empirical analysis agree that the level of ambient radio noise was directly proportional to the rate of IoT radio activities. The slopes of the regression lines demonstrate that 80% of the analyzed data developed augmenting trends. Approximately 20% of the data show declining trends.

Self-C2AD: Enhancing CA Auditing in IoT with Self-Enforcement Based on an SM2 Signature Algorithm

Malicious certificate authorities (CAs) pose a significant threat to the security of the Internet of Things (IoT). Existing CA auditing schemes primarily rely on passive detection and public data collection, lacking real-time and comprehensive monitoring. In this paper, we propose a novel double-authentication preventing signature scheme based on an SM2 algorithm, referred to as Dap-SM2. We further enhance its functionality by introducing Self-C2AD, a CA auditing mechanism with self-enforcement. In our proposed mechanism, any malicious CA that generates two certificates with different descriptions (such as public key and basic information) for the same IoT device will immediately lose its private key. To ensure the security of our proposed scheme, we provide a detailed security analysis of Dap-SM2. The analysis demonstrates that our Self-C2AD mechanism meets the necessary security requirements, offering robust protection against malicious CAs. Additionally, we conduct an efficiency evaluation and present experimental data to illustrate the promising potential of our construction for future IoT applications. By introducing the Dap-SM2 scheme and the Self-C2AD mechanism, we address the critical issue of malicious CAs in the IoT domain. Our approach provides real-time and comprehensive auditing capabilities, surpassing the limitations of existing schemes. The security analysis confirms the effectiveness of Dap-SM2, while the efficiency evaluation and experimental data demonstrate its suitability for practical IoT applications. In summary, our work presents a novel solution to combat the threat of malicious CAs in the IoT context. The Dap-SM2 scheme, coupled with the Self-C2AD mechanism, offers enhanced security and real-time auditing capabilities. The security analysis validates the robustness of our approach, while the efficiency evaluation and experimental data showcase its potential for future IoT deployments.

A secure and efficient AKE scheme for IoT devices using PUF and cancellable biometrics

As the Internet of Things (IoT) continues to grow, there is an increasing need for secure and efficient authentication protocols for IoT devices. The design of IoT authentication schemes requires several essential features such as real two-factor security, Key Compromise Impersonation (KCI) resilience, and Perfect Forward Secrecy (PFS). In this paper, we perform a cryptanalysis of two state-of-the-art PUF-based Authentication and Key Exchange(AKE) schemes and show informally how they fail to achieve real two-factor security, PFS and suffer KCI, user traceability, and database attacks. To address these limitations, we propose an improved two-party AKE scheme between the user-device and the server based on a combination of a physically unclonable function (PUF) and a cancellable biometric (CB). Our proposed scheme provides real two-factor security, KCI resilience, PFS, and user untraceability, achieving all desired properties based on the Computational Diffie–Hellman (CDH) assumption in the random oracle model. To establish the security of our proposed scheme, we utilize formal analysis methods, including the RoR model and ProVerif, and perform an informal security analysis to demonstrate its resilience against potential attacks. Additionally, we analyze the computational and communication performance of our proposed scheme against related PUF-based schemes. Our proposed scheme exhibits a computational and communicational feasible overhead for deployment in the context of the IoT, with a lower magnitude than most previous related PUF-based schemes.Overall, our proposed scheme provides an efficient and secure authentication solution for IoT devices, with improved security and reduced communication overhead compared to previous PUF-based schemes. The combination of formal and informal analysis provides robust evidence of the strength and reliability of our proposed scheme, and its scalability makes it a viable solution for large-scale IoT deployments.